Towards Global Federated Genome-Wide Association Meta-Analysis Using GA4GH TES

2026-09-02

AI Summary

本文提出一种基于GA4GH TES的隐私保护联邦GWAS元分析方法，通过各站点计算并仅传输汇总统计结果而非个体基因型数据，解决了跨机构和国界的数据共享难题。

Abstract

Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic data from being centrally pooled across institutional or national borders. We present a privacy-preserving federated GWAS meta-analysis pipeline built on the APPFL framework, in which each site computes local GWAS summary statistics and transmits only aggregate results, never individual-level genotypes. Analysis is executed through a global network of Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES) endpoints, which allows computation to move to the data rather than the reverse. The server performs inverse-variance-weighted fixed-effect meta-analysis and returns aggregated results to all sites, while HiveWatch, our developed geographic observability toolkit, provides real-time monitoring of distributed task execution. In a five-site simulation over 100,000 synthetic individuals and roughly 240,000 variants for Type 2 Diabetes and Body Mass Index, the federated meta-analysis reproduces the association signal expected from a pooled analysis without centralizing any genotype data, showing that standards-based task execution and federated learning enables a practical privacy-preserving infrastructure for international GWAS meta-analysis.

Problem

• Genome-wide association studies
• Privacy regulations
• Data-residency constraints
• Federated learning
• GA4GH TES

Innovation

• privacy-preserving
• federated GWAS meta-analysis
• GA4GH TES
• APPFL framework
• HiveWatch